In appearance-based methods for object detection and recognition, typical images representative of the objects under consideration are manually extracted and used to find eigenimages in a training procedure. Eigenimages represent the major components of the object's appearance features. In the detection phase, similar appearance features of the objects are recognized by using projections on the eigenimages. Examples of this typical method are common in the art (see, e.g., Turk and Pentland, “Face recognition using eigenfaces” Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.586–591, 1991). A difficulty with the typical method is that image brightness and contrast values in the detection phase may vary significantly from those values used in the training set, leading to detection failures. Unfortunately, when there is a detection failure using the typical method, the missed image must then be added to the training set and a re-training must be performed.
In the appearance-based methods, using multiresolution has been a common practice to reduce computational costs in the detection phase. However, eigenimages for each image resolution are first obtained by independent procedures, thereby increasing the computational burden in the training stage.